millaguie/llama.cpp
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llama.cpp/common/train.cpp
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train : finetune LORA (#2632)
2023-09-28 20:40:11 +02:00
#include "train.h"
#include "common.h"
#include <random>
#include <sstream>
#include <functional>
struct random_normal_distribution {
std::mt19937 gen;
std::normal_distribution<float> rd;
float min;
float max;
};
struct random_uniform_distribution {
std::mt19937 gen;
std::uniform_real_distribution<float> rd;
};
struct train_state * init_train_state() {
struct train_state * state = new struct train_state;
state->train_its = 0;
state->train_samples = 0;
state->train_tokens = 0;
state->train_epochs = 0;
state->shuffle_samples_hash = 0;
state->shuffle_sample_count = 0;
state->shuffle_next_sample = 0;
state->shuffle_rng_state_current = "";
state->shuffle_rng_state_next = "";
state->opt = new struct ggml_opt_context;
state->opt->ctx = NULL;
state->opt->params = ggml_opt_default_params(GGML_OPT_ADAM);
state->opt->loss_after = 0.0f;
return state;
}
void free_train_state(struct train_state * state) {
delete state->opt;
delete state;
}
struct random_normal_distribution * init_random_normal_distribution(
int seed, float mean, float std, float min, float max
) {
struct random_normal_distribution * rnd = (struct random_normal_distribution *) malloc(sizeof(struct random_normal_distribution));
rnd->gen = std::mt19937(seed);
rnd->rd = std::normal_distribution<float>{mean, std};
rnd->min = min;
rnd->max = max;
return rnd;
}
struct random_uniform_distribution * init_random_uniform_distribution(int seed, float min, float max) {
struct random_uniform_distribution * rnd = (struct random_uniform_distribution *) malloc(sizeof(struct random_uniform_distribution));
rnd->gen = std::mt19937(seed);
rnd->rd = std::uniform_real_distribution<float>{min, max};
return rnd;
}
void free_random_normal_distribution (struct random_normal_distribution * rnd) {
free(rnd);
}
void free_random_uniform_distribution(struct random_uniform_distribution * rnd) {
free(rnd);
}
struct ggml_tensor * randomize_tensor_normal(struct ggml_tensor * tensor, struct random_normal_distribution * rnd) {
float scale = 1.0f; // xavier
switch (tensor->n_dims) {
case 1:
scale /= sqrtf((float) tensor->ne[0]);
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0]);
*dst = scale * frand_normal(rnd);
}
break;
case 2:
scale /= sqrtf((float) tensor->ne[0]+tensor->ne[1]);
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]);
*dst = scale * frand_normal(rnd);
}
}
break;
case 3:
scale /= sqrtf((float) tensor->ne[0]+tensor->ne[1]);
for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2]);
*dst = scale * frand_normal(rnd);
}
}
}
break;
case 4:
scale /= sqrtf((float) tensor->ne[0]+tensor->ne[1]);
for (int i3 = 0; i3 < tensor->ne[3]; i3++) {
for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]);
*dst = scale * frand_normal(rnd);
}
}
}
}
break;
default:
die("Unsupported tensor->n_dims");
};
return tensor;
}
struct ggml_tensor * randomize_tensor_uniform(struct ggml_tensor * tensor, struct random_uniform_distribution * rnd) {
switch (tensor->n_dims) {
case 1:
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0]);
*dst = frand_uniform(rnd);
}
break;
case 2:
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]);
*dst = frand_uniform(rnd);
}
}
break;
case 3:
for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2]);
*dst = frand_uniform(rnd);
}
}
}
break;
case 4:
for (int i3 = 0; i3 < tensor->ne[3]; i3++) {
for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]);
*dst = frand_uniform(rnd);
}
}
}
}
break;
default:
die("Unsupported tensor->n_dims");
};
return tensor;
}
float frand() {
return (float)rand()/((float)(RAND_MAX) + 1.0f);
}
float frand_normal(struct random_normal_distribution * rnd) {
return fclamp(rnd->rd(rnd->gen), rnd->min, rnd->max);
}
float frand_uniform(struct random_uniform_distribution * rnd) {
return rnd->rd(rnd->gen);
}
int clamp(const int v, const int min, const int max) {
return ((v < min) ? (min) : (v > max) ? (max) : v);
}
float fclamp(const float v, const float min, const float max) {
return ((v < min) ? (min) : (v > max) ? (max) : v);
}
void assert_shape_1d(struct ggml_tensor * tensor, int64_t ne0) {
GGML_ASSERT(tensor->n_dims == 1);
GGML_ASSERT(tensor->ne[0] == ne0);
}
void assert_shape_2d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1) {
GGML_ASSERT(tensor->n_dims == 2);
GGML_ASSERT(tensor->ne[0] == ne0);
GGML_ASSERT(tensor->ne[1] == ne1);
}
void assert_shape_3d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2) {
GGML_ASSERT(tensor->n_dims == 3);
GGML_ASSERT(tensor->ne[0] == ne0);
GGML_ASSERT(tensor->ne[1] == ne1);
GGML_ASSERT(tensor->ne[2] == ne2);
}
void assert_shape_4d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) {
GGML_ASSERT(tensor->n_dims == 4);
GGML_ASSERT(tensor->ne[0] == ne0);
GGML_ASSERT(tensor->ne[1] == ne1);
GGML_ASSERT(tensor->ne[2] == ne2);
GGML_ASSERT(tensor->ne[3] == ne3);
}
int64_t get_example_targets_batch(
struct llama_context * lctx,
struct ggml_tensor * tokens_input,
struct ggml_tensor * target_probs,
int64_t example_id,
const size_t * samples_offs,
const size_t * samples_begin,
const size_t * samples_size,
size_t samples_count,
const llama_token * train_data,
size_t n_train_data,
bool separate_with_eos,
bool separate_with_bos,
bool fill_with_next_samples,
bool sample_random_offsets
) {
GGML_ASSERT(samples_count > 0);
GGML_ASSERT(tokens_input->n_dims == 2);
GGML_ASSERT(target_probs->n_dims == 3);
int64_t n_vocab = target_probs->ne[0];
int64_t n_tokens = tokens_input->ne[0];
int64_t n_batch = tokens_input->ne[1];
GGML_ASSERT(n_vocab == target_probs->ne[0]);
GGML_ASSERT(n_tokens == target_probs->ne[1]);
GGML_ASSERT(n_batch == target_probs->ne[2]);
int64_t used_samples = 0;
ggml_set_f32(target_probs, 0.0f);
llama : remove token functions with context args in favor of model (#3720)
2023-10-23 12:40:03 -07:00
llama_token bos = llama_token_bos(llama_get_model(lctx));
llama_token eos = llama_token_eos(llama_get_model(lctx));
train : finetune LORA (#2632)
2023-09-28 20:40:11 +02:00
// printf("%s: example_id=%d n_batch=%d n_train_samples=%zu\n", __func__, example_id, n_batch, n_train_samples);
for (int k=0; k<n_batch; ++k) {
// printf("%s: batch %d\n", __func__, k);
size_t sample_idx = (example_id + used_samples) % samples_count;
size_t sample_offs = sample_random_offsets ? samples_offs[sample_idx] : 0;
size_t sample_begin = samples_begin[sample_idx];
size_t sample_size = samples_size[sample_idx];
++used_samples;
// printf("%s: sample_idx=%zu sample=%zu\n", __func__, sample_idx, sample);
GGML_ASSERT(sample_begin+sample_size-1 < n_train_data);
ggml_set_i32_nd(tokens_input, 0, k, 0, 0, bos);
bool sample_separation_eos = !separate_with_eos;
bool sample_separation_bos = !separate_with_bos;
for (int64_t i=0; i<n_tokens; ++i) {
llama_token token = eos;
if (sample_offs >= sample_size && fill_with_next_samples) {
if (!sample_separation_eos) {
// insert eos token to separate samples
sample_separation_eos = true;
} else if (!sample_separation_bos) {
// insert bos token to separate samples
sample_separation_bos = true;
token = bos;
} else {
// sample separation is done, continue with next sample
sample_separation_eos = !separate_with_eos;
sample_separation_bos = !separate_with_bos;
sample_offs = 0;
sample_idx = (example_id + used_samples) % samples_count;
sample_begin = samples_begin[sample_idx];
sample_size = samples_size[sample_idx];
++used_samples;
}
}
// note: no else-if here
if (sample_offs < sample_size) {
token = clamp(train_data[sample_begin+sample_offs], 0, (llama_token) (n_vocab - 1));
++sample_offs;
}
ggml_set_f32_nd(target_probs, token, (int) i, (int) k, 0, +1.0f);
if (i+1<n_tokens) {
ggml_set_i32_nd(tokens_input, (int) (i + 1), (int) k, 0, 0, token);
}
}
}
return used_samples;
}
void mt19937_set_state(std::mt19937& rng, const std::string& rng_state) {
std::stringstream s_rng_state;
s_rng_state.imbue(std::locale::classic());
s_rng_state.exceptions(std::stringstream::failbit);
s_rng_state.str(rng_state);
s_rng_state >> rng;
}
std::string mt19937_get_state(const std::mt19937& rng) {
std::stringstream s_rng_state;
s_rng_state.imbue(std::locale::classic());
s_rng_state << rng;
return s_rng_state.str();
}
std::string mt19937_seed_to_state(unsigned seed) {
std::mt19937 rng(seed);
return mt19937_get_state(rng);
}
std::string shuffle_samples(
const std::string & rng_state,
size_t * shuffled_offs,
size_t * shuffled_begins,
size_t * shuffled_sizes,
const size_t * begins,
const size_t * sizes,
size_t count) {
if (count == 0) return rng_state;
std::mt19937 rng;
mt19937_set_state(rng, rng_state);
// sort indices by random value for each index
std::vector<size_t> idcs;
{
std::vector<unsigned> rnd;
idcs.resize(count);
rnd.resize(count);
for (unsigned i=0; i<count; ++i) {
idcs[i] = i;
rnd[i] = rng();
}
std::sort(idcs.begin(), idcs.end(), [&rnd](size_t a, size_t b){
// stable sort for reproducibility
return (rnd[a] == rnd[b]) ? (a < b) : (rnd[a] < rnd[b]);
});
}
// create random offsets
for (unsigned i=0; i<count; ++i) {
shuffled_offs[i] = (size_t) ((sizes[idcs[i]] - 1) * ((double) rng() / (double) (rng.max()-1)));
}
// reorder begins and sizes by sorted indices
for (unsigned i=0; i<count; ++i) {
shuffled_begins[i] = begins[idcs[i]];
}
for (unsigned i=0; i<count; ++i) {
shuffled_sizes[i] = sizes[idcs[i]];
}
return mt19937_get_state(rng);
}
size_t hash_combine(size_t h1, size_t h2) {
return h1 ^ (h2 << 1);
}
size_t compute_samples_hash(const char* fn, const size_t* samples_begin, const size_t* samples_size, size_t sample_count) {
std::hash<std::string> h_string;
std::hash<unsigned long long> h_ull;
size_t h = h_string(std::string(fn));
h = hash_combine(h, h_ull((unsigned long long) sample_count));
for (size_t i=0; i< sample_count; ++i) {
h = hash_combine(h, h_ull((unsigned long long) samples_begin[i]));
h = hash_combine(h, h_ull((unsigned long long) samples_size[i]));
}
return h;
}
std::string replace_str(const char * s, const char * needle, const char * replacement) {
std::string str = s;
size_t pos = str.find(needle);
if (pos != std::string::npos) {
str.replace(pos, strlen(needle), replacement);
}
return str;
}
void print_duration(double fmillis) {
if (fmillis < 1000.0f) {
printf("%.1fms", (float) fmillis);
return;
}
const int64_t one_sec = 1000;
const int64_t one_min = one_sec * 60;
const int64_t one_hour = one_min * 60;
const int64_t one_day = one_hour * 24;
int64_t millis = (int64_t) fmillis;
int64_t days = millis/one_day;
int64_t hours = (millis - days*one_day)/one_hour;
int64_t minutes = (millis - days*one_day - hours*one_hour)/one_min;
int64_t seconds = (millis - days*one_day - hours*one_hour - minutes*one_min)/one_sec;
// to print int64_t either cast to (long long int) or use macro PRId64 from <inttypes.h>
if (days > 0) {
printf("%lldd ", (long long int) days);
}
printf("%02lld:%02lld:%02lld", (long long int) hours, (long long int) minutes, (long long int) seconds);
}
float cosine_decay(int64_t step, int64_t decay_steps, float minimum) {
if (step > decay_steps) {
step = decay_steps;
}
const float cosine_decay = 0.50f*(1.0f + cosf(3.14159265359f*step/decay_steps));
const float decay = (1 - minimum)*cosine_decay + minimum;
return decay;
}
float cosine_decay_restart(int64_t step, int64_t decay_steps, float minimum, float restart_step_mult) {
while (step > decay_steps) {
step -= decay_steps;
decay_steps = (int64_t) (restart_step_mult * decay_steps);
}
return cosine_decay(step, decay_steps, minimum);
}
float learning_schedule(
int64_t step,
int64_t warmup_steps,
int64_t cos_decay_steps,
float learning_rate,
float overall_minimum,
float cos_decay_minimum,
float cos_decay_restart_step_mult,
bool enable_restart) {
float result =
(step < warmup_steps)
? (float) step / (float) warmup_steps
: enable_restart
? cosine_decay_restart(
step - warmup_steps,
cos_decay_steps,
cos_decay_minimum,
cos_decay_restart_step_mult)
: cosine_decay(
step,
cos_decay_steps,
cos_decay_minimum);
float min = overall_minimum / learning_rate;
result = min + result * (1.0f - min);
return result;
}
static bool are_same_layout(struct ggml_tensor * a, struct ggml_tensor * b) {
GGML_ASSERT(a != NULL);
GGML_ASSERT(b != NULL);
GGML_ASSERT(a->type == b->type);
GGML_ASSERT(ggml_are_same_shape(a, b));
GGML_ASSERT(ggml_is_contiguous(a) && ggml_is_contiguous(b));
return true;
}
void copy_tensor_by_name(struct ggml_tensor * dst, struct ggml_context * ctx, const char * name) {
if (dst == NULL) {
return;
}
struct ggml_tensor * t = ggml_get_tensor(ctx, name);
GGML_ASSERT(are_same_layout(dst, t));
memcpy(dst->data, t->data, ggml_nbytes(t));
if (strlen(ggml_get_name(dst)) == 0) {
ggml_set_name(dst, name);
}
}
// gguf constants
static const char * LLM_KV_OPTIMIZER_TYPE = "optimizer.type";
static const char * LLM_KV_OPTIMIZER_TYPE_ADAM = "adam";
static const char * LLM_KV_OPTIMIZER_TYPE_LBFGS = "lbfgs";
static const char * LLM_KV_OPTIMIZER_FILE_VERSION = "optimizer.file_version";
static const char * LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT = "optimizer.convergence_past_count";
static const char * LLM_KV_OPTIMIZER_PARAMETER_COUNT = "optimizer.parameter_count";
static const char * LLM_KV_OPTIMIZER_ITERATION_COUNT = "optimizer.iteration_count";
static const char * LLM_KV_OPTIMIZER_JUST_INITIALIZED = "optimizer.just_initialized";
static const char * LLM_KV_OPTIMIZER_ADAM_BEST_LOSS = "optimizer.adam.best_loss";
static const char * LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS = "optimizer.adam.previous_loss";
static const char * LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT = "optimizer.adam.no_improvement_count";
static const char * LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT = "optimizer.lbfgs.approx_hessian_count";
static const char * LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS = "optimizer.lbfgs.best_loss";
static const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP = "optimizer.lbfgs.line_search_step";
static const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J = "optimizer.lbfgs.line_search_j";
static const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K = "optimizer.lbfgs.line_search_k";
static const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END = "optimizer.lbfgs.line_search_end";
static const char * LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT = "optimizer.lbfgs.no_improvement_count";
static const char * LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS = "optimizer.adam.first_moments";
static const char * LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS = "optimizer.adam.second_moments";
static const char * LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES = "optimizer.adam.past_loss_values";
static const char * LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS = "optimizer.lbfgs.current_parameters";
static const char * LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS = "optimizer.lbfgs.previous_parameters";
static const char * LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS = "optimizer.lbfgs.current_gradients";
static const char * LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS = "optimizer.lbfgs.previous_gradients";
static const char * LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION = "optimizer.lbfgs.search_direction";
static const char * LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES = "optimizer.lbfgs.past_loss_values";
static const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA = "optimizer.lbfgs.memory_alpha";
static const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS = "optimizer.lbfgs.memory_ys";
static const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S = "optimizer.lbfgs.memory_s";
static const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y = "optimizer.lbfgs.memory_y";
static const char * LLM_KV_TRAINING_FILE_VERSION = "training.file_version";
static const char * LLM_KV_TRAINING_ITERATION_COUNT = "training.iteration_count";
static const char * LLM_KV_TRAINING_SAMPLE_COUNT = "training.sample_count";
static const char * LLM_KV_TRAINING_TOKEN_COUNT = "training.token_count";
static const char * LLM_KV_TRAINING_EPOCH_COUNT = "training.epoch_count";
static const char * LLM_KV_TRAINING_SHUFFLE_SAMPLES_HASH = "training.shuffle.samples_hash";
static const char * LLM_KV_TRAINING_SHUFFLE_RNG_STATE = "training.shuffle.rng_state";
static const char * LLM_KV_TRAINING_SHUFFLE_SAMPLE_COUNT = "training.shuffle.sample_count";
static const char * LLM_KV_TRAINING_SHUFFLE_NEXT_SAMPLE = "training.shuffle.next_sample";
#define GGUF_GET_KEY(ctx, dst, func, type, req, key) \
{ \
const std::string skey(key); \
const int kid = gguf_find_key(ctx, skey.c_str()); \
if (kid >= 0) { \
enum gguf_type ktype = gguf_get_kv_type(ctx, kid); \
if (ktype != (type)) { \
die_fmt("key %s has wrong type: %s", skey.c_str(), gguf_type_name(ktype)); \
} \
(dst) = func(ctx, kid); \
} else if (req) { \
die_fmt("key not found in model: %s", skey.c_str()); \
} \
}
void load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct ggml_opt_context * opt) {
// NOTE: gguf_context must be initialized with f_ggml_ctx and no_alloc=false, otherwise tensor data can not be read
uint32_t file_version;
GGUF_GET_KEY(fctx, file_version, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_FILE_VERSION);
GGML_ASSERT(file_version == 0);
GGUF_GET_KEY(fctx, opt->params.past, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT);
GGUF_GET_KEY(fctx, opt->iter, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_ITERATION_COUNT);
GGUF_GET_KEY(fctx, opt->just_initialized, gguf_get_val_bool, GGUF_TYPE_BOOL, true, LLM_KV_OPTIMIZER_JUST_INITIALIZED);
uint64_t nx;
GGUF_GET_KEY(fctx, nx, gguf_get_val_u64, GGUF_TYPE_UINT64, true, LLM_KV_OPTIMIZER_PARAMETER_COUNT);
opt->nx = (size_t) nx;
// don't call ggml_opt_init until optimizer type and optimizer specific parameters are know
std::string opt_type;
GGUF_GET_KEY(fctx, opt_type, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_OPTIMIZER_TYPE);
if (opt_type == LLM_KV_OPTIMIZER_TYPE_ADAM) {
opt->params.type = GGML_OPT_ADAM;
GGUF_GET_KEY(fctx, opt->adam.fx_best, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_ADAM_BEST_LOSS);
GGUF_GET_KEY(fctx, opt->adam.fx_prev, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS);
GGUF_GET_KEY(fctx, opt->adam.n_no_improvement, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT);
ggml_opt_init(opt->ctx, opt, opt->params, opt->nx);
copy_tensor_by_name(opt->adam.m, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS);
copy_tensor_by_name(opt->adam.v, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS);
copy_tensor_by_name(opt->adam.pf, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES);
} else if (opt_type == LLM_KV_OPTIMIZER_TYPE_LBFGS) {
opt->params.type = GGML_OPT_LBFGS;
GGUF_GET_KEY(fctx, opt->params.lbfgs.m, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT);
GGUF_GET_KEY(fctx, opt->lbfgs.fx_best, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS);
GGUF_GET_KEY(fctx, opt->lbfgs.step, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP);
GGUF_GET_KEY(fctx, opt->lbfgs.j, gguf_get_val_i32, GGUF_TYPE_INT32, true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J);
GGUF_GET_KEY(fctx, opt->lbfgs.k, gguf_get_val_i32, GGUF_TYPE_INT32, true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K);
GGUF_GET_KEY(fctx, opt->lbfgs.end, gguf_get_val_i32, GGUF_TYPE_INT32, true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END);
GGUF_GET_KEY(fctx, opt->lbfgs.n_no_improvement, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT);
ggml_opt_init(opt->ctx, opt, opt->params, opt->nx);
copy_tensor_by_name(opt->lbfgs.x, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS);
copy_tensor_by_name(opt->lbfgs.xp, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS);
copy_tensor_by_name(opt->lbfgs.g, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS);
copy_tensor_by_name(opt->lbfgs.gp, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS);
copy_tensor_by_name(opt->lbfgs.d, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION);
copy_tensor_by_name(opt->lbfgs.pf, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES);
copy_tensor_by_name(opt->lbfgs.lmal, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA);
copy_tensor_by_name(opt->lbfgs.lmys, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS);
copy_tensor_by_name(opt->lbfgs.lms, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S);
copy_tensor_by_name(opt->lbfgs.lmy, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y);
} else {
die("unknown optimizer type\n");
}
}
void save_opt_context_gguf(struct gguf_context * fctx, struct ggml_opt_context * opt) {
gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_FILE_VERSION, 0);
gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT, opt->params.past);
gguf_set_val_u64(fctx, LLM_KV_OPTIMIZER_PARAMETER_COUNT, (uint64_t) opt->nx);
gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_ITERATION_COUNT, opt->iter);
gguf_set_val_bool(fctx, LLM_KV_OPTIMIZER_JUST_INITIALIZED, opt->just_initialized);
switch (opt->params.type) {
case GGML_OPT_ADAM:
{
gguf_set_val_str(fctx, LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_ADAM);
gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_ADAM_BEST_LOSS, opt->adam.fx_best);
gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS, opt->adam.fx_prev);
gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT, opt->adam.n_no_improvement);
ggml_set_name(opt->adam.m, LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS);
ggml_set_name(opt->adam.v, LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS);
if (opt->adam.pf) {
ggml_set_name(opt->adam.pf, LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES);
}
gguf_add_tensor(fctx, opt->adam.m);
gguf_add_tensor(fctx, opt->adam.v);
if (opt->adam.pf) {
gguf_add_tensor(fctx, opt->adam.pf);
}
} break;
case GGML_OPT_LBFGS:
{
gguf_set_val_str(fctx, LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS);
gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, opt->params.lbfgs.m);
gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS, opt->lbfgs.fx_best);
gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP, opt->lbfgs.step);
gguf_set_val_i32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J, opt->lbfgs.j);
gguf_set_val_i32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K, opt->lbfgs.k);
gguf_set_val_i32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END, opt->lbfgs.end);
gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT, opt->lbfgs.n_no_improvement);
ggml_set_name(opt->lbfgs.x, LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS);
ggml_set_name(opt->lbfgs.xp, LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS);
ggml_set_name(opt->lbfgs.g, LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS);
ggml_set_name(opt->lbfgs.gp, LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS);
ggml_set_name(opt->lbfgs.d, LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION);
if (opt->lbfgs.pf) {
ggml_set_name(opt->lbfgs.pf, LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES);
}
ggml_set_name(opt->lbfgs.lmal, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA);
ggml_set_name(opt->lbfgs.lmys, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS);
ggml_set_name(opt->lbfgs.lms, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S);
ggml_set_name(opt->lbfgs.lmy, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y);
gguf_add_tensor(fctx, opt->lbfgs.x);
gguf_add_tensor(fctx, opt->lbfgs.xp);
gguf_add_tensor(fctx, opt->lbfgs.g);
gguf_add_tensor(fctx, opt->lbfgs.gp);
gguf_add_tensor(fctx, opt->lbfgs.d);
if (opt->lbfgs.pf) {
gguf_add_tensor(fctx, opt->lbfgs.pf);
}
gguf_add_tensor(fctx, opt->lbfgs.lmal);
gguf_add_tensor(fctx, opt->lbfgs.lmys);
gguf_add_tensor(fctx, opt->lbfgs.lms);
gguf_add_tensor(fctx, opt->lbfgs.lmy);
} break;
}
}
bool load_train_state_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct train_state * train) {
if (gguf_find_key(fctx, LLM_KV_TRAINING_FILE_VERSION) < 0) {
return false;
}
uint32_t file_version;
GGUF_GET_KEY(fctx, file_version, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_FILE_VERSION);
GGML_ASSERT(file_version <= 1);
if (file_version == 0) {
GGUF_GET_KEY(fctx, train->train_its, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_ITERATION_COUNT);
GGUF_GET_KEY(fctx, train->train_samples, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_SAMPLE_COUNT);
GGUF_GET_KEY(fctx, train->train_tokens, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_TOKEN_COUNT);
} else if (file_version == 1) {
GGUF_GET_KEY(fctx, train->train_its, gguf_get_val_u64, GGUF_TYPE_UINT64, true, LLM_KV_TRAINING_ITERATION_COUNT);
GGUF_GET_KEY(fctx, train->train_samples, gguf_get_val_u64, GGUF_TYPE_UINT64, true, LLM_KV_TRAINING_SAMPLE_COUNT);
GGUF_GET_KEY(fctx, train->train_tokens, gguf_get_val_u64, GGUF_TYPE_UINT64, true, LLM_KV_TRAINING_TOKEN_COUNT);
GGUF_GET_KEY(fctx, train->train_epochs, gguf_get_val_u64, GGUF_TYPE_UINT64, true, LLM_KV_TRAINING_EPOCH_COUNT);
GGUF_GET_KEY(fctx, train->shuffle_samples_hash, gguf_get_val_u64, GGUF_TYPE_UINT64, false, LLM_KV_TRAINING_SHUFFLE_SAMPLES_HASH);
GGUF_GET_KEY(fctx, train->shuffle_rng_state_current, gguf_get_val_str, GGUF_TYPE_STRING, false, LLM_KV_TRAINING_SHUFFLE_RNG_STATE);
GGUF_GET_KEY(fctx, train->shuffle_sample_count, gguf_get_val_u64, GGUF_TYPE_UINT64, false, LLM_KV_TRAINING_SHUFFLE_SAMPLE_COUNT);
GGUF_GET_KEY(fctx, train->shuffle_next_sample, gguf_get_val_u64, GGUF_TYPE_UINT64, false, LLM_KV_TRAINING_SHUFFLE_NEXT_SAMPLE);
}
load_opt_context_gguf(fctx, f_ggml_ctx, train->opt);
return true;
}
void save_train_state_gguf(struct gguf_context * fctx, struct train_state * train) {
gguf_set_val_u32(fctx, LLM_KV_TRAINING_FILE_VERSION, 1);
gguf_set_val_u64(fctx, LLM_KV_TRAINING_ITERATION_COUNT, train->train_its);
gguf_set_val_u64(fctx, LLM_KV_TRAINING_SAMPLE_COUNT, train->train_samples);
gguf_set_val_u64(fctx, LLM_KV_TRAINING_TOKEN_COUNT, train->train_tokens);
gguf_set_val_u64(fctx, LLM_KV_TRAINING_EPOCH_COUNT, train->train_epochs);
gguf_set_val_u64(fctx, LLM_KV_TRAINING_SHUFFLE_SAMPLES_HASH, (uint64_t) train->shuffle_samples_hash);
gguf_set_val_str(fctx, LLM_KV_TRAINING_SHUFFLE_RNG_STATE, train->shuffle_rng_state_current.c_str());
gguf_set_val_u64(fctx, LLM_KV_TRAINING_SHUFFLE_SAMPLE_COUNT, (uint64_t) train->shuffle_sample_count);
gguf_set_val_u64(fctx, LLM_KV_TRAINING_SHUFFLE_NEXT_SAMPLE, (uint64_t) train->shuffle_next_sample);
save_opt_context_gguf(fctx, train->opt);
}
struct llama_file {
// use FILE * so we don't have to re-open the file to mmap
FILE * fp;
size_t size;
llama_file(const char * fname, const char * mode) {
fp = std::fopen(fname, mode);
if (fp == NULL) {
size = 0;
} else {
seek(0, SEEK_END);
size = tell();
seek(0, SEEK_SET);
}
}
size_t tell() const {
#ifdef _WIN32
__int64 ret = _ftelli64(fp);
#else
long ret = std::ftell(fp);
#endif
GGML_ASSERT(ret != -1); // this really shouldn't fail
return (size_t) ret;
}
void seek(size_t offset, int whence) {
#ifdef _WIN32
int ret = _fseeki64(fp, (__int64) offset, whence);
#else
int ret = std::fseek(fp, (long) offset, whence);
#endif
GGML_ASSERT(ret == 0); // same
}
void read_raw(void * ptr, size_t size) {
if (size == 0) {
return;
}
errno = 0;
std::size_t ret = std::fread(ptr, size, 1, fp);
if (ferror(fp)) {
die_fmt("read error: %s", strerror(errno));
}
if (ret != 1) {
die("unexpectedly reached end of file");
}
}
std::uint32_t read_u32() {
std::uint32_t ret;
read_raw(&ret, sizeof(ret));
return ret;
}
std::string read_string(std::uint32_t len) {
std::vector<char> chars(len);
read_raw(chars.data(), len);
return std::string(chars.data(), len);
}
void write_raw(const void * ptr, size_t size) {
if (size == 0) {
return;
}
errno = 0;
size_t ret = std::fwrite(ptr, size, 1, fp);
if (ret != 1) {
die_fmt("write error: %s", strerror(errno));
}
}
void write_u32(std::uint32_t val) {
write_raw(&val, sizeof(val));
}
~llama_file() {
if (fp) {
std::fclose(fp);
}
}
};
static size_t utf8_len(char src) {
const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
uint8_t highbits = static_cast<uint8_t>(src) >> 4;
return lookup[highbits];
}
// mark each byte with its utf8 unit number.
// returns the number of utf8 characters.
// e.g. when bytes == '\x61\xD0\xB0\x62',
// then utf8_units will become [0,0,1,0]
// utf8_nunits will become [1,2,2,1] and 3 is returned.
// bytes where utf8_units is zero, are the begin of an utf8 character.
static size_t mark_utf8_units(const char* bytes, int * utf8_units, int * utf8_nunits, size_t count) {
size_t offs = 0;
size_t count_utf8 = 0;
while(offs < count) {
int len = (int) utf8_len(bytes[offs]);
for (int i=0; i<len; ++i) {
utf8_units[offs+i] = i;
utf8_nunits[offs+i] = len;
}
offs += len;
++count_utf8;
}
return count_utf8;
}
size_t tokenize_file(
struct llama_context * lctx,
const char * filename,
const std::string & sample_start,
bool include_sample_start,
bool overlapping_samples,
unsigned context_length,
std::vector<llama_token> & out_tokens,
std::vector<size_t> & out_samples_begin,
std::vector<size_t> & out_samples_size) {
struct llama_file f(filename, "rb");
if (f.size == 0) {
out_tokens.clear();
out_samples_begin.clear();
out_samples_size.clear();
printf("%s: warning: empty or not existing training data file '%s'\n",
__func__, filename);
return out_tokens.size();
}
// account for possible leading whitespace that will be added by tokenizer
// e.g. '\t' will be tokenized by llama spm tokenizer to [29871, 12]
const int n_max_tokens_overhead = 1;
std::vector<char> buf;
buf.resize(f.size);
f.read_raw(buf.data(), f.size);
std::vector<int> utf8_units;
std::vector<int> utf8_nunits;
utf8_units.resize(buf.size());
utf8_nunits.resize(buf.size());
mark_utf8_units(buf.data(), utf8_units.data(), utf8_nunits.data(), buf.size());
if (sample_start.size() == 0) {
// tokenize all data at once
out_tokens.resize(buf.size() + n_max_tokens_overhead);
int n_tokens = llama_tokenize(
llama.cpp : split llama_context_params into model and context params (#3301)
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llama_get_model(lctx),
train : finetune LORA (#2632)
2023-09-28 20:40:11 +02:00
buf.data(),
(int) buf.size(),
out_tokens.data(),
(int) out_tokens.size(),
tokenizer : special token handling (#3538)
2023-10-17 17:11:01 +02:00
false, false);
train : finetune LORA (#2632)
2023-09-28 20:40:11 +02:00
if (n_tokens < 0) {
out_tokens.resize(-n_tokens);
n_tokens = llama_tokenize(
llama.cpp : split llama_context_params into model and context params (#3301)
2023-09-28 21:42:38 +02:00
llama_get_model(lctx),
train : finetune LORA (#2632)
2023-09-28 20:40:11 +02:00
buf.data(),
(int) buf.size(),
out_tokens.data(),
(int) out_tokens.size(),
tokenizer : special token handling (#3538)
2023-10-17 17:11:01 +02:00
false, false);
train : finetune LORA (#2632)
2023-09-28 20:40:11 +02:00
}
if (n_tokens >= 0) {
out_tokens.resize(n_tokens);
}
// generate sample starts at all token positions
out_samples_begin.clear();
out_samples_begin.push_back(0);
out_samples_size.push_back(std::min((size_t) context_length, out_tokens.size()));
size_t end = (out_tokens.size() >= context_length) ? (out_tokens.size() - context_length) : 0;
for (size_t sample_begin = 1; sample_begin < end; ++sample_begin) {
out_samples_begin.push_back(sample_begin);
out_samples_size.push_back(context_length);
}
} else {
// split data into samples and tokenize each sample
std::string data_str(buf.data(), buf.size());
out_samples_begin.clear();
out_samples_size.clear();
out_tokens.clear();
// find all positions of pattern sample_start
size_t sample_begin = data_str.find(sample_start, 0);
while (sample_begin != std::string::npos) {
out_samples_begin.push_back(sample_begin);
const size_t search_start = sample_begin + sample_start.size();
sample_begin = data_str.find(sample_start, search_start);
}
if (out_samples_begin.size() == 0) {
printf("%s: warning: sample start pattern '%s' not found. inserting single sample at data begin\n",
__func__, sample_start.c_str());
out_samples_begin.push_back(0);
}
out_samples_size.resize(out_samples_begin.size(), 0);
std::vector<char> buf_sample;
std::vector<llama_token> tok_sample;
const size_t sample_begin_offset = (include_sample_start ? 0 : sample_start.size());
size_t found_too_big_sample = 0;
size_t found_too_small_sample = 0;
size_t found_empty_sample = 0;
size_t found_min_sample_size = SIZE_MAX;
size_t found_max_sample_size = 0;
size_t max_token_text_size = 0;
llama.cpp : split llama_context_params into model and context params (#3301)
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int n_vocab = llama_n_vocab(llama_get_model(lctx));
train : finetune LORA (#2632)
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for (llama_token token=0; token < n_vocab; ++token) {
max_token_text_size = std::max(
max_token_text_size,
llama : remove token functions with context args in favor of model (#3720)
2023-10-23 12:40:03 -07:00
strlen(llama_token_get_text(llama_get_model(lctx), token)));
train : finetune LORA (#2632)
2023-09-28 20:40:11 +02:00
}
// upper bound of context byte length.
// strings with this byte length should always tokenize to at least context_length tokens.
size_t context_byte_len = max_token_text_size*context_length;
for (unsigned i=0; i<out_samples_begin.size(); ++i) {
// determine sample begin and end from pattern positions
size_t sample_begin = out_samples_begin[i] + sample_begin_offset;
size_t sample_end = overlapping_samples
? std::min(
data_str.size(),
sample_begin + context_byte_len)
: (i+1 < out_samples_begin.size()
? out_samples_begin[i+1]
: data_str.size());
if (sample_end < utf8_units.size() && utf8_units[sample_end] > 0) {
// sample end is in the middle of an utf8 character.
// advance sample_end to the begin of the next utf8 character.
sample_end += utf8_nunits[sample_end] - utf8_units[sample_end];
}
size_t sample_size = sample_end - sample_begin;
if (sample_size == 0) {
++found_empty_sample;
}
if (sample_size > 0) {
// llama_tokenize expects zero terminated string,
// copy sample into buffer and zero terminate it.
buf_sample.resize(sample_size);
memcpy(buf_sample.data(), data_str.data() + sample_begin, sample_size);
// printf("sample: '%s'\n", buf_sample.data());
// tokenize the sample
tok_sample.resize(buf_sample.size() + n_max_tokens_overhead);
llama.cpp : split llama_context_params into model and context params (#3301)
2023-09-28 21:42:38 +02:00
int n_tokens = llama_tokenize(llama_get_model(lctx),
train : finetune LORA (#2632)
2023-09-28 20:40:11 +02:00
buf_sample.data(),
(int) buf_sample.size(),
tok_sample.data(),
(int) tok_sample.size(),
tokenizer : special token handling (#3538)
2023-10-17 17:11:01 +02:00
false, false);
train : finetune LORA (#2632)
2023-09-28 20:40:11 +02:00
if (n_tokens < 0) {
tok_sample.resize(-n_tokens);
llama.cpp : split llama_context_params into model and context params (#3301)
2023-09-28 21:42:38 +02:00
n_tokens = llama_tokenize(llama_get_model(lctx),
train : finetune LORA (#2632)
2023-09-28 20:40:11 +02:00
buf_sample.data(),
(int) buf_sample.size(),
tok_sample.data(),
(int) tok_sample.size(),
tokenizer : special token handling (#3538)
2023-10-17 17:11:01 +02:00
false, false);
train : finetune LORA (#2632)
2023-09-28 20:40:11 +02:00
GGML_ASSERT(n_tokens >= 0);
}
GGML_ASSERT(n_tokens <= (int) tok_sample.size());
if ((size_t) n_tokens > context_length) {
++found_too_big_sample;
} else if ((size_t) n_tokens < context_length) {
++found_too_small_sample;
}
found_max_sample_size = std::max(found_max_sample_size, (size_t) n_tokens);
found_min_sample_size = std::min(found_min_sample_size, (size_t) n_tokens);
// write out tokens, start and size of sample
// overwrite the string start position with the token start position
out_samples_begin[i] = out_tokens.size();
out_samples_size[i] = (size_t) n_tokens;
out_tokens.insert(out_tokens.end(), tok_sample.begin(), tok_sample.begin() + n_tokens);
} else {
out_samples_begin[i] = out_tokens.size();
out_samples_size[i] = 0;
}
}
if (found_too_big_sample > 0) {
printf("%s: warning: found %zu samples (max length %zu) that exceed context length of %u. samples will be cut off.\n",
__func__, found_too_big_sample, found_max_sample_size, context_length);
}
if (found_too_small_sample > 0) {
printf("%s: warning: found %zu samples (min length %zu) that are shorter than context length of %u.\n",
__func__, found_too_small_sample, found_min_sample_size, context_length);
}
if (found_empty_sample) {
printf("%s: warning: found %zu empty samples.\n",
__func__, found_empty_sample);
}
}
printf("%s: total number of samples: %zu\n",
__func__, out_samples_begin.size());
GGML_ASSERT(out_samples_begin.size() == out_samples_size.size());
return out_tokens.size();
}
std::string get_train_filename(const char * filename, const char * pattern_it, const char * latest, int64_t iteration) {
std::string sit = (iteration >= 0) ? std::to_string(iteration) : std::string(latest);
return replace_str(filename, pattern_it, sit.c_str());
}
struct train_params_common get_default_train_params_common() {
struct train_params_common params;
params.fn_train_data = "shakespeare.txt";
params.fn_checkpoint_in = "checkpoint.gguf";
params.fn_checkpoint_out = "checkpoint-ITERATION.gguf";
params.pattern_fn_it = "ITERATION";
params.fn_latest = "LATEST";
params.print_usage = false;
params.save_every = 10;
params.seed = -1;
params.n_ctx = 128;
params.n_threads = 6;
params.n_batch = 8;
params.n_gradient_accumulation = 1;
params.n_epochs = -1;
finetune : add -ngl parameter (#3762)
2023-11-01 04:49:04 -07:00
params.n_gpu_layers = 0;
train : finetune LORA (#2632)
2023-09-28 20:40:11 +02:00
params.custom_n_ctx = false;
params.use_flash = true;
params.use_checkpointing = true;
params.sample_start = "";
params.include_sample_start = false;
params.escape = false;
params.overlapping_samples = false;
params.fill_with_next_samples = false;
params.separate_with_eos = false;
params.separate_with_bos = true;
params.sample_random_offsets = false;
params.force_reshuffle = false;
params.opt_past = 0;
params.opt_delta = 1e-5f;
params.opt_max_no_improvement = 0;
params.warmup = 100;
params.cos_decay_steps = 1000;
params.cos_decay_restart = 1.1f;
params.cos_decay_min = 0.1f;
params.enable_restart = false;
params.adam_n_iter = 256;
params.adam_alpha = 1e-3f;
params.adam_min_alpha = 0;
params.adam_decay = 1e-1f;
params.adam_decay_min_ndim = 2;
params.adam_beta1 = 0.9f;
params.adam_beta2 = 0.999f;
params.adam_gclip = 1.0f;
params.adam_eps_f = 0.0f;
finetune : add -ngl parameter (#3762)
2023-11-01 04:49:04 -07:00
train : finetune LORA (#2632)
2023-09-28 20:40:11 +02:00
return params;
}
void print_common_train_usage(int /*argc*/, char ** /*argv*/, const struct train_params_common * params) {
// fprintf(stderr, "usage: %s [options]\n", argv[0]);
// fprintf(stderr, "\n");
// fprintf(stderr, "options:\n");
// fprintf(stderr, " -h, --help show this help message and exit\n");
fprintf(stderr, " --train-data FNAME path from which to load training data (default '%s')\n", params->fn_train_data);
fprintf(stderr, " --checkpoint-in FNAME path from which to load training checkpoint (default '%s')\n", params->fn_checkpoint_in);
fprintf(stderr, " --checkpoint-out FNAME path to save training checkpoint (default '%s')\n", params->fn_checkpoint_out);
fprintf(stderr, " --pattern-fn-it STR pattern in output filenames to be replaced by iteration number (default '%s')\n", params->pattern_fn_it);
fprintf(stderr, " --fn-latest STR string to use instead of iteration number for saving latest output (default '%s')\n", params->fn_latest);
fprintf(stderr, " --save-every N save checkpoint and lora every N iterations. Disabled when N <= 0. (default '%d')\n", params->save_every);
fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for -1)\n");
fprintf(stderr, " -c N, --ctx N Context size used during training (default %d)\n", params->n_ctx);
fprintf(stderr, " -t N, --threads N Number of threads (default %d)\n", params->n_threads);
fprintf(stderr, " -b N, --batch N Parallel batch size (default %d)\n", params->n_batch);
fprintf(stderr, " --grad-acc N Number of gradient accumulation steps (simulates larger batch size of batch*gradacc) (default %d)\n", params->n_gradient_accumulation);
fprintf(stderr, " --sample-start STR Sets the starting point for samples after the specified pattern. If empty use every token position as sample start. (default '%s')\n", params->sample_start.c_str());
fprintf(stderr, " --include-sample-start Include the sample start in the samples. (default off)\n");
fprintf(stderr, " --escape process sample start escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n");
fprintf(stderr, " --overlapping-samples Samples my overlap, will include sample-start of second and following samples. When off, samples will end at begin of next sample. (default off)\n");
fprintf(stderr, " --fill-with-next-samples Samples shorter than context length will be followed by the next (shuffled) samples. (default off)\n");
fprintf(stderr, " --separate-with-eos When fill-with-next-samples, insert end-of-sequence token between samples.%s\n", params->separate_with_eos ? " (default)" : "");
fprintf(stderr, " --separate-with-bos When fill-with-next-samples, insert begin-of-sequence token between samples.%s\n", params->separate_with_bos ? " (default)" : "");
fprintf(stderr, " --no-separate-with-eos When fill-with-next-samples, don't insert end-of-sequence token between samples.%s\n", !params->separate_with_eos ? " (default)" : "");
fprintf(stderr, " --no-separate-with-bos When fill-with-next-samples, don't insert begin-of-sequence token between samples.%s\n", !params->separate_with_bos ? " (default)" : "");
fprintf(stderr, " --sample-random-offsets Use samples beginning at random offsets. Together with fill-with-next-samples this may help for training endless text generation.%s\n", params->sample_random_offsets ? " (default)" : "");
fprintf(stderr, " --force-reshuffle Force a reshuffling of data at program start, otherwise the shuffling of loaded checkpoint is resumed.\n");
fprintf(stderr, " --no-flash Don't use flash attention \n");
fprintf(stderr, " --use-flash Use flash attention (default)\n");
fprintf(stderr, " --no-checkpointing Don't use gradient checkpointing\n");
fprintf(stderr, " --use-checkpointing Use gradient checkpointing (default)\n");
fprintf(stderr, " --warmup N Only for Adam optimizer. Number of warmup steps (default %d)\n", params->warmup);
fprintf(stderr, " --cos-decay-steps N Only for Adam optimizer. Number of cosine decay steps (default %d)\n", params->cos_decay_steps);
fprintf(stderr, " --cos-decay-restart N Only for Adam optimizer. Increase of cosine decay steps after restart (default %f)\n", params->cos_decay_restart);
fprintf(stderr, " --cos-decay-min N Only for Adam optimizer. Cosine decay minimum (default %f)\n", params->cos_decay_min);
fprintf(stderr, " --enable-restart N Only for Adam optimizer. Enable restarts of cos-decay %s\n", params->enable_restart ? "(default)" : "");
fprintf(stderr, " --disable-restart N Only for Adam optimizer. Disable restarts of cos-decay %s\n", !params->enable_restart ? "(default)" : "");
fprintf(stderr, " --opt-past N Number of optimization iterations to track for delta convergence test. Disabled when zero. (default %d)\n", params->opt_past);
fprintf(stderr, " --opt-delta N Maximum delta for delta convergence test. Disabled when <= zero. (default %f)\n", params->opt_delta);
fprintf(stderr, " --opt-max-no-improvement N Maximum number of optimization iterations with no improvement. Disabled when <= zero. (default %d)\n", params->opt_max_no_improvement);
fprintf(stderr, " --epochs N Maximum number epochs to process. (default %d)\n", params->n_epochs);
fprintf(stderr, " --adam-iter N Maximum number of Adam optimization iterations for each batch (default %d)\n", params->adam_n_iter);
fprintf(stderr, " --adam-alpha N Adam learning rate alpha (default %f)\n", params->adam_alpha);
fprintf(stderr, " --adam-min-alpha N Adam minimum learning rate alpha - including warmup phase (default %f)\n", params->adam_min_alpha);
fprintf(stderr, " --adam-decay N AdamW weight decay. Values greater zero enable AdamW instead of regular Adam. (default %f)\n", params->adam_decay);
fprintf(stderr, " --adam-decay-min-ndim N Minimum number of tensor dimensions to apply AdamW weight decay. Weight decay is not applied to tensors with less n_dims. (default %d)\n", params->adam_decay_min_ndim);
fprintf(stderr, " --adam-beta1 N AdamW beta1 in interval [0,1). How much to smooth the first moment of gradients. (default %f)\n", params->adam_beta1);
fprintf(stderr, " --adam-beta2 N AdamW beta2 in interval [0,1). How much to smooth the second moment of gradients. (default %f)\n", params->adam_beta2);
fprintf(stderr, " --adam-gclip N AdamW gradient clipping. Disabled when zero. (default %f)\n", params->adam_gclip);
fprintf(stderr, " --adam-epsf N AdamW epsilon for convergence test. Disabled when <= zero. (default %f)\n", params->adam_eps_f);
fprintf(stderr, "\n");
}
bool consume_common_train_arg(
int argc, char ** argv, int * idx, struct train_params_common * params, bool * invalid_param
) {
int& i = *idx;
std::string arg = argv[i];
const std::string arg_prefix = "--";
if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
std::replace(arg.begin(), arg.end(), '_', '-');
}
if (arg == "--train-data") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->fn_train_data = argv[i];
} else if (arg == "--checkpoint-in") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->fn_checkpoint_in = argv[i];
} else if (arg == "--checkpoint-out") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->fn_checkpoint_out = argv[i];
} else if (arg == "--pattern-fn-it") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->pattern_fn_it = argv[i];
} else if (arg == "--fn-latest") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->fn_latest = argv[i];
} else if (arg == "--save-every") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->save_every = std::stoi(argv[i]);
} else if (arg == "-s" || arg == "--seed") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->seed = std::stoi(argv[i]);
} else if (arg == "-c" || arg == "--ctx") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->n_ctx = std::stoi(argv[i]);
params->custom_n_ctx = true;
} else if (arg == "-t" || arg == "--threads") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->n_threads = std::stoi(argv[i]);
} else if (arg == "-b" || arg == "--batch") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->n_batch = std::stoi(argv[i]);
} else if (arg == "--grad-acc") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->n_gradient_accumulation = std::max(1, std::stoi(argv[i]));
} else if (arg == "--sample-start") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->sample_start = std::string(argv[i]);
} else if (arg == "--escape") {
params->escape = true;
} else if (arg == "--include-sample-start") {
params->include_sample_start = true;
} else if (arg == "--overlapping-samples") {
params->overlapping_samples = true;
} else if (arg == "--fill-with-next-samples") {
params->fill_with_next_samples = true;
} else if (arg == "--separate-with-eos") {
params->separate_with_eos = true;
} else if (arg == "--separate-with-bos") {
params->separate_with_bos = true;
} else if (arg == "--no-separate-with-eos") {
params->separate_with_eos = false;
} else if (arg == "--no-separate-with-bos") {
params->separate_with_bos = false;
} else if (arg == "--sample-random-offsets") {
params->sample_random_offsets = true;
} else if (arg == "--force-reshuffle") {
params->force_reshuffle = true;
} else if (arg == "--no-flash") {
params->use_flash = false;
} else if (arg == "--use-flash") {
params->use_flash = true;
} else if (arg == "--no-checkpointing") {
params->use_checkpointing = false;
} else if (arg == "--use-checkpointing") {
params->use_checkpointing = true;
} else if (arg == "--warmup") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->warmup = std::stoi(argv[i]);
} else if (arg == "--cos-decay-steps") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->cos_decay_steps = std::stoi(argv[i]);
} else if (arg == "--cos-decay-restart") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->cos_decay_restart = std::stof(argv[i]);
} else if (arg == "--cos-decay-min") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->cos_decay_min = std::stof(argv[i]);
} else if (arg == "--enable-restart") {
params->enable_restart = true;
} else if (arg == "--disable-restart") {
params->enable_restart = false;
} else if (arg == "--opt-past") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->opt_past = std::stoi(argv[i]);
} else if (arg == "--opt-delta") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->opt_delta = std::stof(argv[i]);
} else if (arg == "--opt-max-no-improvement") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->opt_max_no_improvement = std::stoi(argv[i]);
} else if (arg == "--adam-epsf") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->adam_eps_f = std::stof(argv[i]);
} else if (arg == "--epochs") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->n_epochs = std::stoi(argv[i]);
} else if (arg == "--adam-iter") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->adam_n_iter = std::stoi(argv[i]);
} else if (arg == "--adam-alpha") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->adam_alpha = std::stof(argv[i]);
} else if (arg == "--adam-min-alpha") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->adam_min_alpha = std::stof(argv[i]);
} else if (arg == "--adam-decay") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->adam_decay = std::stof(argv[i]);
} else if (arg == "--adam-decay-min-ndim") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->adam_decay_min_ndim = std::stoi(argv[i]);
} else if (arg == "--adam-beta1") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->adam_beta1 = std::stof(argv[i]);
} else if (arg == "--adam-beta2") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->adam_beta2 = std::stof(argv[i]);
} else if (arg == "--adam-gclip") {
if (++i >= argc) {
*invalid_param = true;
return true;
}
params->adam_gclip = std::stof(argv[i]);
} else if (arg == "-h" || arg == "--help") {
params->print_usage = true;
return true;
} else {
return false;
}
return true;
}
void finish_processing_train_args(struct train_params_common * params) {
if (params->escape) {
process_escapes(params->sample_start);
}
}
void train_opt_callback(void * vdata, int accum_step, float * sched, bool * cancel) {
struct train_opt_callback_data * data = (struct train_opt_callback_data *) vdata;
struct train_params_common * params = data->params;
struct train_state * train = data->train;
struct ggml_opt_context * opt = train->opt;
int n_batch = params->n_batch;
int n_ctx = params->n_ctx;
if (accum_step == 0) {
// time measurement
int64_t now = ggml_time_ms();
if (now > data->last_time && opt->iter > data->first_iter) {
double dt = (double) (now - data->last_time);
if (data->millis_per_iter == 0.0) {
data->millis_per_iter = dt;
} else {
const double gain = 0.7;
data->millis_per_iter = data->millis_per_iter*(1.0-gain) + dt*gain;
}
}
double remaining_millis = 0.0;
if (data->millis_per_iter > 0.0) {
const int n_iter = params->adam_n_iter;
const int done_iter = opt->iter - data->first_iter;
const int remaining_iter = n_iter - done_iter;
remaining_millis = remaining_iter * data->millis_per_iter;
}
// file saving
const bool save_now = (params->save_every > 0) && (opt->iter - data->last_save_iter >= params->save_every);
if (save_now) {
int new_iters = opt->iter - data->last_save_iter;
train->train_its += new_iters;
train->train_tokens += new_iters * opt->params.n_gradient_accumulation * n_batch * n_ctx;
if (data->save_cb) {
data->save_cb(data->save_data, train);
}
data->last_save_iter = opt->iter;
}
// exclude file saving from time measurement, by measuring last_time after saving
data->last_time = ggml_time_ms();
*sched = learning_schedule(
opt->iter,
params->warmup,
params->cos_decay_steps,
params->adam_alpha,
params->adam_min_alpha,
params->cos_decay_min,
params->cos_decay_restart,
params->enable_restart);
int impr_plot = -(int)(1 + (opt->loss_before - opt->loss_after) * 10.0f + 0.5f);
if (impr_plot > 0) impr_plot = 0;
ggml : fix rope + llama minor optimizations (#3560)
2023-10-20 10:02:12 +00:00
if (std::isnan(opt->loss_before) || std::isnan(opt->loss_after)) impr_plot = 0;
train : finetune LORA (#2632)
2023-09-28 20:40:11 +02:00
printf("%s: iter=%6d sample=%zu/%zu sched=%f loss=%f",
__func__, opt->iter, std::min(1+train->shuffle_next_sample, train->shuffle_sample_count), train->shuffle_sample_count,
*sched, opt->loss_after);
if (data->millis_per_iter > 0) {
printf(" dt=");
print_duration(data->millis_per_iter);
printf(" eta=");
print_duration(remaining_millis);
}
float improvement = opt->loss_before - opt->loss_after;
const float plot_scale = 10.0f;
int bar_len = (int)(1 + improvement*plot_scale + 0.5);
printf(" |");
for (int i=0; i<bar_len; ++i) {
printf("-");
}
printf(">");
printf("\n");
}
int64_t used_samples = get_example_targets_batch(
data->lctx,
data->tokens_input,
data->target_probs,
train->shuffle_next_sample,
data->shuffled_samples_offs,
data->shuffled_samples_begin,
data->shuffled_samples_size,
data->samples_count,
data->tokens_data,
data->tokens_size,
params->separate_with_eos,
params->separate_with_bos,
params->fill_with_next_samples,
params->sample_random_offsets);
train->train_samples += used_samples;
train->shuffle_next_sample += used_samples;
if (train->shuffle_next_sample >= train->shuffle_sample_count) {
++train->train_epochs;
printf("%s: reshuffle samples. completed epochs: %llu\n", __func__, (long long unsigned) train->train_epochs);
// note: we may have used some samples from the current shuffling more than once
train->shuffle_rng_state_current = train->shuffle_rng_state_next;
train->shuffle_rng_state_next = shuffle_samples(
train->shuffle_rng_state_current,
data->shuffled_samples_offs,
data->shuffled_samples_begin,
data->shuffled_samples_size,
data->samples_begin,
data->samples_size,
data->samples_count);
train->shuffle_next_sample = 0;
}
const bool last_epoch_reached = (params->n_epochs > 0 && (int64_t) train->train_epochs - data->first_epoch >= params->n_epochs);
if (last_epoch_reached) {
// allow optimization iteration at last epoch to be completed before canceling
if (data->iter_at_last_epoch < 0) {
data->iter_at_last_epoch = opt->iter;
} else if (opt->iter > data->iter_at_last_epoch) {
*cancel = true;
}
}
}
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